Improving Quality of Service and HTTPS DDoS Detection in MEC Environment With a Cyber Deception-Based Architecture

Creating a cyber deception framework for 5G networks, particularly in IoT and cellular applications, is complex due to critical constraints in managing resources, meeting low latency demands, and addressing security concerns. While cloud computing aids in alleviating some limitations, it often falls short in meeting low-latency requirements. Multi-Access Edge Computing (MEC) has emerged as a solution by bringing resources closer to User Equipment (UEs) to reduce latency. Various MEC architectures have leveraged Software Defined Networking (SDN), Network Function Virtualization (NFV), Service Function Chaining (SFC), Network Slicing (NS), decision-making systems, and deception components. However, none have integrated these technologies comprehensively to achieve superior Quality of Service (QoS) and strengthen security. In this paper, we unify SDN, NFV, SFC, NS, decision-making technologies, and deception to efficiently manage MEC server resources and lure attackers. We utilize cyber deception metrics, including request collection rates over time and variations in request numbers concerning different botnet sizes. Moreover, we meticulously address QoS parameters such as latency, computing, storage, and bandwidth resources. Our approach initiates with a mathematical model for MEC server resource allocation, introducing a novel architecture that reduces bandwidth, computing, and storage resource usage. We introduce a cyber deception strategy utilizing uniform distribution and random selection to divert potential attackers. Simulations validate efficient resource management, notably reducing end-to-end latency for requests processed on the edge and in the cloud. This enhancement improves QoS within the MEC system and provides valuable insights for advancing decision-making technologies.